Overview

Dataset statistics

Number of variables11
Number of observations582
Missing cells4
Missing cells (%)0.1%
Duplicate rows13
Duplicate rows (%)2.2%
Total size in memory50.1 KiB
Average record size in memory88.2 B

Variable types

Numeric9
Categorical2

Alerts

Dataset has 13 (2.2%) duplicate rowsDuplicates
Alamine_Aminotransferase is highly overall correlated with Asparate_AminotransferaseHigh correlation
Albumin is highly overall correlated with Albumin_Globulin_Ratio and 1 other fieldsHigh correlation
Albumin_Globulin_Ratio is highly overall correlated with AlbuminHigh correlation
Asparate_Aminotransferase is highly overall correlated with Alamine_Aminotransferase and 2 other fieldsHigh correlation
Direct_Bilirubin is highly overall correlated with Asparate_Aminotransferase and 1 other fieldsHigh correlation
Total_Bilirubin is highly overall correlated with Asparate_Aminotransferase and 1 other fieldsHigh correlation
Total_protiens is highly overall correlated with AlbuminHigh correlation

Reproduction

Analysis started2024-04-07 06:58:34.955372
Analysis finished2024-04-07 06:58:48.286490
Duration13.33 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct72
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.71134
Minimum4
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:48.411525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile18
Q133
median45
Q357.75
95-th percentile72
Maximum90
Range86
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation16.181921
Coefficient of variation (CV)0.36191984
Kurtosis-0.55553284
Mean44.71134
Median Absolute Deviation (MAD)12
Skewness-0.026391787
Sum26022
Variance261.85457
MonotonicityNot monotonic
2024-04-07T12:28:48.567919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 34
 
5.8%
45 25
 
4.3%
50 23
 
4.0%
38 21
 
3.6%
42 21
 
3.6%
48 20
 
3.4%
32 20
 
3.4%
55 18
 
3.1%
40 17
 
2.9%
46 16
 
2.7%
Other values (62) 367
63.1%
ValueCountFrequency (%)
4 2
0.3%
6 1
 
0.2%
7 2
0.3%
8 1
 
0.2%
10 1
 
0.2%
11 1
 
0.2%
12 2
0.3%
13 4
0.7%
14 2
0.3%
15 1
 
0.2%
ValueCountFrequency (%)
90 1
 
0.2%
85 1
 
0.2%
84 1
 
0.2%
78 1
 
0.2%
75 14
2.4%
74 4
 
0.7%
73 2
 
0.3%
72 8
1.4%
70 9
1.5%
69 2
 
0.3%

Gender
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
Male
441 
Female
141 

Length

Max length6
Median length4
Mean length4.4845361
Min length4

Characters and Unicode

Total characters2610
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 441
75.8%
Female 141
 
24.2%

Length

2024-04-07T12:28:48.718205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T12:28:48.859002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
male 441
75.8%
female 141
 
24.2%

Most occurring characters

ValueCountFrequency (%)
e 723
27.7%
a 582
22.3%
l 582
22.3%
M 441
16.9%
F 141
 
5.4%
m 141
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 723
27.7%
a 582
22.3%
l 582
22.3%
M 441
16.9%
F 141
 
5.4%
m 141
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 723
27.7%
a 582
22.3%
l 582
22.3%
M 441
16.9%
F 141
 
5.4%
m 141
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 723
27.7%
a 582
22.3%
l 582
22.3%
M 441
16.9%
F 141
 
5.4%
m 141
 
5.4%

Total_Bilirubin
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3032646
Minimum0.4
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:48.962460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.6
Q10.8
median1
Q32.6
95-th percentile16.375
Maximum75
Range74.6
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation6.2139264
Coefficient of variation (CV)1.881147
Kurtosis37.105388
Mean3.3032646
Median Absolute Deviation (MAD)0.35
Skewness4.9034664
Sum1922.5
Variance38.612881
MonotonicityNot monotonic
2024-04-07T12:28:49.113169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 91
15.6%
0.7 76
 
13.1%
0.9 57
 
9.8%
0.6 46
 
7.9%
1 28
 
4.8%
1.1 19
 
3.3%
1.8 14
 
2.4%
1.4 13
 
2.2%
1.3 12
 
2.1%
1.7 11
 
1.9%
Other values (103) 215
36.9%
ValueCountFrequency (%)
0.4 1
 
0.2%
0.5 5
 
0.9%
0.6 46
7.9%
0.7 76
13.1%
0.8 91
15.6%
0.9 57
9.8%
1 28
 
4.8%
1.1 19
 
3.3%
1.2 8
 
1.4%
1.3 12
 
2.1%
ValueCountFrequency (%)
75 1
0.2%
42.8 1
0.2%
32.6 1
0.2%
30.8 1
0.2%
30.5 2
0.3%
27.7 1
0.2%
27.2 1
0.2%
26.3 1
0.2%
25 1
0.2%
23.3 1
0.2%

Direct_Bilirubin
Real number (ℝ)

HIGH CORRELATION 

Distinct80
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.488488
Minimum0.1
Maximum19.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:49.283470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.2
median0.3
Q31.3
95-th percentile8.4
Maximum19.7
Range19.6
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation2.8103242
Coefficient of variation (CV)1.8880396
Kurtosis11.329042
Mean1.488488
Median Absolute Deviation (MAD)0.2
Skewness3.2093365
Sum866.3
Variance7.8979223
MonotonicityNot monotonic
2024-04-07T12:28:49.497778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 194
33.3%
0.1 62
 
10.7%
0.3 51
 
8.8%
0.8 22
 
3.8%
0.4 21
 
3.6%
0.5 20
 
3.4%
0.6 16
 
2.7%
1 13
 
2.2%
1.3 12
 
2.1%
1.6 11
 
1.9%
Other values (70) 160
27.5%
ValueCountFrequency (%)
0.1 62
 
10.7%
0.2 194
33.3%
0.3 51
 
8.8%
0.4 21
 
3.6%
0.5 20
 
3.4%
0.6 16
 
2.7%
0.7 11
 
1.9%
0.8 22
 
3.8%
0.9 7
 
1.2%
1 13
 
2.2%
ValueCountFrequency (%)
19.7 1
0.2%
18.3 1
0.2%
17.1 1
0.2%
14.2 1
0.2%
14.1 1
0.2%
13.7 1
0.2%
12.8 1
0.2%
12.6 2
0.3%
12.1 1
0.2%
11.8 2
0.3%

Alkaline_Phosphotase
Real number (ℝ)

Distinct263
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean290.7543
Minimum63
Maximum2110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:49.730699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile137
Q1175.25
median208
Q3298
95-th percentile698.55
Maximum2110
Range2047
Interquartile range (IQR)122.75

Descriptive statistics

Standard deviation243.10893
Coefficient of variation (CV)0.83613186
Kurtosis17.719313
Mean290.7543
Median Absolute Deviation (MAD)50
Skewness3.7615886
Sum169219
Variance59101.952
MonotonicityNot monotonic
2024-04-07T12:28:49.964009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
298 11
 
1.9%
215 11
 
1.9%
198 11
 
1.9%
195 10
 
1.7%
190 10
 
1.7%
180 10
 
1.7%
145 9
 
1.5%
182 9
 
1.5%
158 9
 
1.5%
282 8
 
1.4%
Other values (253) 484
83.2%
ValueCountFrequency (%)
63 1
0.2%
75 1
0.2%
90 1
0.2%
92 2
0.3%
97 1
0.2%
98 1
0.2%
100 2
0.3%
102 1
0.2%
103 1
0.2%
105 1
0.2%
ValueCountFrequency (%)
2110 1
0.2%
1896 1
0.2%
1750 1
0.2%
1630 1
0.2%
1620 1
0.2%
1580 1
0.2%
1550 1
0.2%
1420 1
0.2%
1350 2
0.3%
1124 1
0.2%

Alamine_Aminotransferase
Real number (ℝ)

HIGH CORRELATION 

Distinct152
Distinct (%)26.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.824742
Minimum10
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:50.129709image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15
Q123
median35
Q360.75
95-th percentile232
Maximum2000
Range1990
Interquartile range (IQR)37.75

Descriptive statistics

Standard deviation182.7577
Coefficient of variation (CV)2.2611603
Kurtosis50.495354
Mean80.824742
Median Absolute Deviation (MAD)15
Skewness6.5439689
Sum47040
Variance33400.375
MonotonicityNot monotonic
2024-04-07T12:28:50.276303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 25
 
4.3%
20 23
 
4.0%
22 18
 
3.1%
18 17
 
2.9%
21 17
 
2.9%
28 17
 
2.9%
30 15
 
2.6%
48 14
 
2.4%
15 14
 
2.4%
24 13
 
2.2%
Other values (142) 409
70.3%
ValueCountFrequency (%)
10 4
 
0.7%
11 2
 
0.3%
12 10
1.7%
13 4
 
0.7%
14 8
1.4%
15 14
2.4%
16 7
1.2%
17 8
1.4%
18 17
2.9%
19 6
 
1.0%
ValueCountFrequency (%)
2000 1
0.2%
1680 1
0.2%
1630 1
0.2%
1350 1
0.2%
1250 2
0.3%
950 1
0.2%
875 2
0.3%
790 1
0.2%
779 1
0.2%
622 1
0.2%

Asparate_Aminotransferase
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.06873
Minimum10
Maximum4929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:50.442904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile15.05
Q125
median42
Q387
95-th percentile400.95
Maximum4929
Range4919
Interquartile range (IQR)62

Descriptive statistics

Standard deviation289.14188
Coefficient of variation (CV)2.6269212
Kurtosis150.68844
Mean110.06873
Median Absolute Deviation (MAD)21
Skewness10.53834
Sum64060
Variance83603.025
MonotonicityNot monotonic
2024-04-07T12:28:50.664285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 16
 
2.7%
21 14
 
2.4%
30 14
 
2.4%
20 14
 
2.4%
22 13
 
2.2%
28 13
 
2.2%
25 13
 
2.2%
34 12
 
2.1%
24 12
 
2.1%
32 12
 
2.1%
Other values (167) 449
77.1%
ValueCountFrequency (%)
10 1
 
0.2%
11 2
 
0.3%
12 5
0.9%
13 3
 
0.5%
14 8
1.4%
15 11
1.9%
16 9
1.5%
17 8
1.4%
18 8
1.4%
19 11
1.9%
ValueCountFrequency (%)
4929 1
 
0.2%
2946 1
 
0.2%
1600 1
 
0.2%
1500 1
 
0.2%
1050 2
0.3%
960 1
 
0.2%
950 1
 
0.2%
850 4
0.7%
844 1
 
0.2%
794 1
 
0.2%

Total_protiens
Real number (ℝ)

HIGH CORRELATION 

Distinct58
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.482646
Minimum2.7
Maximum9.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:50.863919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile4.605
Q15.8
median6.6
Q37.2
95-th percentile8.1
Maximum9.6
Range6.9
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.0863056
Coefficient of variation (CV)0.16757132
Kurtosis0.22785145
Mean6.482646
Median Absolute Deviation (MAD)0.7
Skewness-0.28402549
Sum3772.9
Variance1.1800598
MonotonicityNot monotonic
2024-04-07T12:28:51.029727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 32
 
5.5%
6 30
 
5.2%
6.8 27
 
4.6%
6.9 25
 
4.3%
6.2 24
 
4.1%
7.1 22
 
3.8%
7.2 21
 
3.6%
8 20
 
3.4%
7.3 18
 
3.1%
6.1 18
 
3.1%
Other values (48) 345
59.3%
ValueCountFrequency (%)
2.7 1
 
0.2%
2.8 1
 
0.2%
3 1
 
0.2%
3.6 3
0.5%
3.7 1
 
0.2%
3.8 2
0.3%
3.9 2
0.3%
4 2
0.3%
4.1 2
0.3%
4.3 3
0.5%
ValueCountFrequency (%)
9.6 1
 
0.2%
9.5 1
 
0.2%
9.2 2
 
0.3%
8.9 1
 
0.2%
8.7 1
 
0.2%
8.6 3
 
0.5%
8.5 5
0.9%
8.4 3
 
0.5%
8.3 3
 
0.5%
8.2 8
1.4%

Albumin
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1415808
Minimum0.9
Maximum5.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:51.180549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.8
Q12.6
median3.1
Q33.8
95-th percentile4.395
Maximum5.5
Range4.6
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.79617604
Coefficient of variation (CV)0.25343167
Kurtosis-0.39210503
Mean3.1415808
Median Absolute Deviation (MAD)0.6
Skewness-0.042638014
Sum1828.4
Variance0.63389629
MonotonicityNot monotonic
2024-04-07T12:28:51.294720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
3 45
 
7.7%
4 37
 
6.4%
2.9 29
 
5.0%
3.1 28
 
4.8%
3.2 26
 
4.5%
3.9 25
 
4.3%
2.7 24
 
4.1%
2.5 24
 
4.1%
3.5 23
 
4.0%
2 21
 
3.6%
Other values (30) 300
51.5%
ValueCountFrequency (%)
0.9 2
 
0.3%
1 1
 
0.2%
1.4 3
 
0.5%
1.5 3
 
0.5%
1.6 8
 
1.4%
1.7 3
 
0.5%
1.8 12
2.1%
1.9 7
 
1.2%
2 21
3.6%
2.1 14
2.4%
ValueCountFrequency (%)
5.5 2
 
0.3%
5 1
 
0.2%
4.9 4
 
0.7%
4.8 2
 
0.3%
4.7 3
 
0.5%
4.6 4
 
0.7%
4.5 6
1.0%
4.4 8
1.4%
4.3 14
2.4%
4.2 12
2.1%

Albumin_Globulin_Ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)11.9%
Missing4
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean0.94714533
Minimum0.3
Maximum2.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.7 KiB
2024-04-07T12:28:51.444525image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median0.94
Q31.1
95-th percentile1.5
Maximum2.8
Range2.5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.31986292
Coefficient of variation (CV)0.33771261
Kurtosis3.2705459
Mean0.94714533
Median Absolute Deviation (MAD)0.16
Skewness0.99074173
Sum547.45
Variance0.10231229
MonotonicityNot monotonic
2024-04-07T12:28:51.580528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 106
18.2%
0.8 65
11.2%
0.9 58
10.0%
0.7 53
9.1%
1.1 46
7.9%
1.2 35
 
6.0%
0.6 31
 
5.3%
0.5 29
 
5.0%
1.3 25
 
4.3%
1.4 17
 
2.9%
Other values (59) 113
19.4%
ValueCountFrequency (%)
0.3 4
 
0.7%
0.35 1
 
0.2%
0.37 1
 
0.2%
0.39 1
 
0.2%
0.4 14
2.4%
0.45 1
 
0.2%
0.46 1
 
0.2%
0.47 2
 
0.3%
0.48 1
 
0.2%
0.5 29
5.0%
ValueCountFrequency (%)
2.8 1
 
0.2%
2.5 2
 
0.3%
1.9 1
 
0.2%
1.85 2
 
0.3%
1.8 3
0.5%
1.72 1
 
0.2%
1.7 4
0.7%
1.66 1
 
0.2%
1.6 5
0.9%
1.58 2
 
0.3%

Target
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.7 KiB
1
415 
2
167 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters582
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Length

2024-04-07T12:28:51.710345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T12:28:51.810892image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Most occurring characters

ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 582
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 415
71.3%
2 167
28.7%

Interactions

2024-04-07T12:28:46.665861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.353647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:36.783753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.985142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.314784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.483032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.916619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:43.437174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.403395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.764351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.446850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:36.885375image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.094306image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.412527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.660199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.025937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:43.563865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.556842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.879640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.605658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.018624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.253829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.531147image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.882617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.234738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:43.738039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.737936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.013692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.745793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.147819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.434062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.671630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.083756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.461241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:44.205222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.941887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.125884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.846754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.264916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.583764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.816933image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.202159image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.621656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:44.418183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.069669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.287010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:35.966871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.434608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.734880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.983027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.358412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.823734image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:44.683754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.214197image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.417567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:36.082736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.583236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:38.862024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.151354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.504861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:42.965321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:44.887005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.334206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.552598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:36.223325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.734610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.033919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.275717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.665274image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:43.136134image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.106932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.444547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:47.669234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:36.401189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:37.867763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:39.182372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:40.382643image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:41.785702image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:43.276885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:45.243250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2024-04-07T12:28:46.554679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2024-04-07T12:28:51.910184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
AgeAlamine_AminotransferaseAlbuminAlbumin_Globulin_RatioAlkaline_PhosphotaseAsparate_AminotransferaseDirect_BilirubinGenderTargetTotal_BilirubinTotal_protiens
Age1.000-0.065-0.262-0.2500.061-0.0150.1110.1250.2000.117-0.175
Alamine_Aminotransferase-0.0651.000-0.052-0.0840.4100.7730.4100.0000.1140.435-0.018
Albumin-0.262-0.0521.0000.754-0.171-0.205-0.2330.0350.155-0.2220.779
Albumin_Globulin_Ratio-0.250-0.0840.7541.000-0.322-0.210-0.2990.0000.205-0.2860.273
Alkaline_Phosphotase0.0610.410-0.171-0.3221.0000.3950.3670.1040.2030.3830.014
Asparate_Aminotransferase-0.0150.773-0.205-0.2100.3951.0000.5020.0000.0930.508-0.084
Direct_Bilirubin0.1110.410-0.233-0.2990.3670.5021.0000.0780.2150.959-0.019
Gender0.1250.0000.0350.0000.1040.0000.0781.0000.0690.198-0.090
Target0.2000.1140.1550.2050.2030.0930.2150.0691.000-0.3060.033
Total_Bilirubin0.1170.435-0.222-0.2860.3830.5080.9590.198-0.3061.000-0.019
Total_protiens-0.175-0.0180.7790.2730.014-0.084-0.019-0.0900.033-0.0191.000

Missing values

2024-04-07T12:28:47.902259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T12:28:48.187469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAsparate_AminotransferaseTotal_protiensAlbuminAlbumin_Globulin_RatioTarget
062Male10.95.5699641007.53.20.741
162Male7.34.149060687.03.30.891
258Male1.00.418214206.83.41.001
372Male3.92.019527597.32.40.401
446Male1.80.720819147.64.41.301
526Female0.90.215416127.03.51.001
629Female0.90.320214116.73.61.101
717Male0.90.320222197.44.11.202
855Male0.70.229053586.83.41.001
957Male0.60.121051595.92.70.801
AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAsparate_AminotransferaseTotal_protiensAlbuminAlbumin_Globulin_RatioTarget
57232Male3.71.661250886.21.90.401
57332Male12.16.051548926.62.40.501
57432Male25.013.756041887.92.52.501
57532Male15.08.228958805.32.20.701
57632Male12.78.419028475.42.60.901
57760Male0.50.150020345.91.60.372
57840Male0.60.19835316.03.21.101
57952Male0.80.224548496.43.21.001
58031Male1.30.518429326.83.41.001
58138Male1.00.321621247.34.41.502

Duplicate rows

Most frequently occurring

AgeGenderTotal_BilirubinDirect_BilirubinAlkaline_PhosphotaseAlamine_AminotransferaseAsparate_AminotransferaseTotal_protiensAlbuminAlbumin_Globulin_RatioTarget# duplicates
018Male0.80.2282721405.52.50.8012
130Male1.60.4332841395.62.70.9012
231Male0.60.117548346.03.71.6012
334Male4.12.02898757315.02.71.1012
436Male0.80.215829396.02.20.5022
536Male5.32.314532925.12.61.0022
638Female2.61.241059575.63.00.8022
739Male1.90.918042627.44.31.3812
840Female0.90.32932322456.83.10.8012
942Male8.94.527231615.82.00.5012